Multi-Sensor Fusion Localization of Indoor Mobile Robot
Why this work is in the frame
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Bibliographic record
Abstract
Localization is critical for map building in visual SLAM (Simultaneous Localization and Map). Currently, accurate localization systems, such as Motion Capture, are expensive and, as many of them, not easy for re-configuration, a property essential for field robot test. This paper proposes a camera-odometry fusion method, which bases on a camera-marker system of low-cost and easy for re-configuration. The technology is based on odometers by combining two different sensor modules and PES using EKF (Extended Kalman Filter). A critical problem of EKF is the unknown PES (Position Estiamte System) variance, which is always set as a constant in previous works. In this paper, we solve this problem by using PES marker-pair, instead of a solo marker, to directly estimate the variance of PES localization. Experimental results in indoor environment demonstrate that the proposed approach substantially improves the localization accuracy of SLAM compared with PES only and odometry only. The position error is found to be less than 40mm of our system.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it